---
license: mit
pipeline_tag: feature-extraction
tags:
- chemistry
- molecular-embeddings
- smiles
- descriptor
---

# **Chemical Dice Integrator (CDI)**

**CDI (Chemical Dice Integrator)** is a high-performance deep learning framework designed to unify heterogeneous chemical representations into a single, information-rich latent space. By fusing six complementary molecular embeddings, CDI produces a consolidated vector optimized for large-scale cheminformatics, bioinformatics, and AI-driven molecular discovery tasks.

---

<div align="center">
  <img src="Images/CDI.png" alt="ChemicalDice Integrator Overview" width="750">
</div>

---

## 📚 Contents

* [Overview](#overview)
* [Prerequisites](#prerequisites)
* [Docker & API Setup](#⚡-docker--api-setup)
* [Python Implementation](#🐍-python-implementation)
* [R Implementation](#📊-r-implementation)

---

## Overview

CDI performs unsupervised integration of **six distinct molecular embeddings**:

* **Quantum Descriptors** (Electronic properties)
* **Bioactivity Signatures** (Biological response profiles)
* **Language Model Embeddings** (Transformer-based SMILES)
* **Graph Representations** (Structural topology)
* **Physicochemical Profiles** (LogP, MW, solubility)
* **2D Image Features** (Computer vision-derived)

Each compound’s features are combined into a **single latent embedding** optimized for **QSAR modeling**, **virtual screening**, and **drug-target interaction prediction**.

---

## 🛠 Prerequisites & System Requirements

Before installing the ChemicalDice ecosystem, ensure your system meets the following requirements. The framework is designed to run within a **Docker** container to handle deep learning dependencies, with **Python** or **R** acting as the client interface.

### 1. Hardware Requirements

* **GPU (Recommended):** NVIDIA GPU with CUDA support for high-throughput embedding generation.
* **Memory:** Minimum 8GB RAM (16GB+ recommended for large-scale CSV processing).
* **Disk Space:** ~10GB for the Docker image and model weights.

### 2. Core Environments

| Component | Required Version | Purpose |
| --- | --- | --- |
| **Docker** | 20.10+ | Runs the CDI API and deep learning backend. |
| **NVIDIA Container Toolkit** | Latest | Enables GPU acceleration inside Docker. |
| **Python** | 3.8 — 3.11 | Required for the Python client and RdkIt integration. |
| **R** | 4.0.0+ | Required for the R interface users. |

### 3. API & Model Access

* **Hugging Face Account:** You will need access to the [SuvenduK/ChemicalDice](https://huggingface.co/SuvenduK/ChemicalDice) repository to pull the necessary model weights.
* **Network Access:** Ensure your firewall allows communication on port `8002` (or your chosen local port) for the REST API.

---

## ⚡ Docker & API Setup

The recommended way to use ChemicalDiceIntegrator model is through the provided **Docker environment**.

### 1. Build and Run the Docker Environment

The Docker build creates an image that exposes a REST API for generating embeddings via HTTP requests.

```bash
# Build the image
docker build -t chemicaldice-api .

# Run the container with GPU support (port 8002)
docker run -d --gpus all -p 8002:8000 --name chemicaldice-container chemicaldice-api

```

### 2. Access Documentation & Test

* **Swagger UI:** [http://localhost:8002/docs](http://localhost:8002/docs)
* **Test with Curl:**
```bash
curl -X 'POST' 'http://localhost:8002/predict-single-smile' \
  -H 'Content-Type: application/json' \
  -d '{"smiles": "CCO"}'

```



---

## 🐍 Python Implementation

### Installation

```bash
pip install numpy pandas rdkit tqdm requests
pip install -i https://test.pypi.org/simple/ ChemicalDice

```

### Usage

```python
from ChemicalDice import smiles_to_embeddings
import pandas as pd

# Load or create your data
df = pd.DataFrame({'SMILES': ['CCO', 'c1ccccc1', 'CC(=O)Oc1ccccc1C(=O)O']})
df.to_csv("smiles.csv", index=False)

# Generate embeddings via local API
CDI_embeddings = smiles_to_embeddings.collect_features_from_csv(
    filepath="smiles.csv",
    convert_to_canonical=False,
    URL="http://localhost:8002/"
)

print(CDI_embeddings.head())

```

---

## 📊 R Implementation

### 1. Installation

```r
# Install CRAN dependencies
install.packages(c("httr", "data.table", "progress", "jsonlite", "reticulate", "curl", "remotes"))

# Install ChemicalDice R package from GitHub
remotes::install_github("the-ahuja-lab/ChemicalDice", subdir = "R-package")

```

### 2. Configuration & Setup

Before use, configure `reticulate` to point to a Python environment containing **RDKit**.

```r
library(ChemicalDice)
library(reticulate)

# Configure your Python/Conda environment
use_condaenv("my_rdkit_env", required = TRUE)

# Ensure RDKit is available
py_require("rdkit")
rdkit <- import("rdkit.Chem", convert = TRUE)

```

### 3. Usage in R

The R interface allows you to process SMILES data frames and interface seamlessly with the local Docker API.

```r
# Define your SMILES data
smiles_data <- data.frame(
  SMILES = c("CCO", "c1ccccc1", "CC(=O)Oc1ccccc1C(=O)O")
)

# Generate integrated embeddings
# Ensure the Docker container is running on port 8002
embeddings <- collect_features_from_df(
  df = smiles_data,
  convert_to_canonical = FALSE,
  URL = "http://localhost:8002/"
)

# View results
print(head(embeddings))

```

> **Note:** For the R implementation, ensure your Python environment is set up with `conda install -c conda-forge rdkit`.